Axillary odour build-up in knit fabrics following multiple use cycles
Bibliographic record
Abstract
Purpose – Fibre content can influence the intensity of odour that develops within clothing fabrics. However, little is known about how effective laundering is at removing malodours in clothing which differ by fibre type. The purpose of this paper is to investigate whether a selected cotton fabric differed in odour intensity following multiple wear and wash cycles compared to a polyester fabric. Design/methodology/approach – Eight (male and female) participants wore bisymmetrical cotton/polyester t-shirts during 20 exercise sessions over a ten-week trial period. Odour was evaluated via a sensory panel, bacterial populations were counted and selected odorous volatile organic compounds were measured with comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry detection. Analysis occurred both before and after the final (20th) wash cycle. Findings – Findings showed that laundering was effective in reducing overall odour intensity (p0.001) and bacterial populations (p0.001) in both cotton and polyester fabrics. Odour was most intense on polyester fabrics following wear, not just before, but also after washing (p0.001); although, no differences in bacterial counts were found between fibre types (p>0.05). Chemical analysis found C4-C8 chained carboxylic acids on both types of unwashed fabrics, although they were more prevalent on polyester. Originality/value – The findings suggest that the build-up of odour in polyester fabrics may be cumulative as important odorants such as the carboxylic acids were not as effectively removed from polyester compared to cotton.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".